Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-05

AUTHORS

Warren A Cheung, BF Francis Ouellette, Wyeth W Wasserman

ABSTRACT

BACKGROUND: Using annotations to the articles in MEDLINE®/PubMed®, over six thousand chemical compounds with pharmacological actions have been tracked since 1996. Medical Subject Heading Over-representation Profiles (MeSHOPs) quantitatively leverage the literature associated with biological entities such as diseases or drugs, providing the opportunity to reposition known compounds towards novel disease applications. METHODS: A MeSHOP is constructed by counting the number of times each medical subject term is assigned to an entity-related research publication in the MEDLINE database and calculating the significance of the count by comparing against the count of the term in a background set of publications. Based on the expectation that drugs suitable for treatment of a disease (or disease symptom) will have similar annotation properties to the disease, we successfully predict drug-disease associations by comparing MeSHOPs of diseases and drugs. RESULTS: The MeSHOP comparison approach delivers an 11% improvement over bibliometric baselines. However, novel drug-disease associations are observed to be biased towards drugs and diseases with more publications. To account for the annotation biases, a correction procedure is introduced and evaluated. CONCLUSIONS: By explicitly accounting for the annotation bias, unexpectedly similar drug-disease pairs are highlighted as candidates for drug repositioning research. MeSHOPs are shown to provide a literature-supported perspective for discovery of new links between drugs and diseases based on pre-existing knowledge. More... »

PAGES

s3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1755-8794-6-s2-s3

DOI

http://dx.doi.org/10.1186/1755-8794-6-s2-s3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010386267

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23819887


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